As output from DAM, \({\hat{x}}\)  is fed into the CNN backbone, a ResNet50 [1]} whose input channels are changed to four and the part after its final convolutional layer is removed. For the reason that image compositions not only mean high-level semantics such as subject locations but also include lower-level features like region complexity, we introduce a multi-scale strategy on the last two convolutional blocks following FPN [2]}. There is a slight difference that we do not generate layouts on each scale separately like detection networks often do, but concatenate the fused and upsampled features as one. We denote \(F_j\)  the feature maps of the \({j}\) -th convolutional block. Then multi-scale features can be computed as
\(\begin{aligned}F^{^{\prime }}_j = \mathbf {Conv_{11}}(F_j)\;;\quad F^{up}_j = \mathbf {Upsample}(F^{^{\prime }}_j)\;;\\F^{fused}_j = \mathbf {Cancat}(F^{up}_j, \mathbf {Conv_{33}}(F^{up}_j+F^{^{\prime }}_{j-1}))\end{aligned}\) 
